Radiographic imaging, in its simplest expression, is an X-ray beam traversing an object and a detector relating the overall attenuation per ray. From this conceptual definition, several steps are required to properly construct an image. Several elements affect how the actual image reconstruction is performed.
In computed tomography, the operation that transforms an N-dimension image into an N-dimension set of line integrals is called a forward-projection. One example of this operation is the physical process that generates an X-ray image of an object. After logarithmic conversion, an X-ray image is well approximated as the line integral projection of the distribution of the object's linear attenuation coefficient. The transpose operation is called back-projection. This technique is used in filtered back-projection and in iterative reconstruction, which are used in conventional reconstruction algorithms.
The methods for forward- and back-projection in X-ray and CT systems can be generally classified as ray-driven methods or pixel-driven methods. A critical drawback associated with these methods is that they introduce artifacts in the constructed image. A distance-driven method addresses the above issues. However, the distance-driven method of forward- and back-projections incurs a significant number of processing operations (i.e., weighting and multiplication operations) that tend to increase the image reconstruction time.
Further, an iterative reconstruction system typically performs back-projection on all views within a set of views before any forward-projection occurs. In other words, a data partition size in the iterative reconstruction system includes the whole volume. Specifically, N views are back projected to create a volume of pixels. The volume is then looped over and forward projected to create a new set of N views. This processing requires a large amount of bandwidth to move the volumes in and out of the system. In addition, since the data partition is the size of the volume, there is very little opportunity to parallelize the forward- and back-projection processing. Thus, the performance of the iterative reconstruction system is constrained.
Accordingly, methods for improving the overall image reconstruction time and bandwidth usage in back- and forward-projection processes are needed.